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Generative AI
Generative AI is a broad term for any type of AI system capable of creating new forms of humanlike creative content — such as text, images, music, audio, video and more — using generative models.
Generative models are algorithms that learn the various patterns and structures of input training data before generating new outputs with similar characteristics.
The massive popularity of ChatGPT — perhaps the most successful proof of concept in the history of technology — has pushed generative AI front and center among business and IT leaders.
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How does generative AI work?
Generative AI models rely on various neural network architectures (e.g., transformers, generative adversarial networks (GANs), variational auto-encoders (VAEs) and diffusion networks) to produce their humanlike outputs. Each works a bit differently.
Transformers
Transformers use mechanisms called attention modules to efficiently process input data in parallel, learn their relationships, and then generate new forms of creative content. Examples include GPT-3 and DALL-E.
Variational auto-encoders (VAEs)
VAEs leverage encoders to transform input data into latent vectors, while decoders learn to reconstruct samples of these vectors into original outputs that closely resemble the input data. Examples include VAE-GAN and MusicVAE.
Generative adversarial networks (GANs)
GANs leverage adversarial learning — a technique to simultaneously train two neural networks against each other in a competitive game-like scenario — to improve the accuracy of both models over time. Examples include StyleGAN and CycleGAN.
Diffusion networks
A type of deep neural network that adds noise to a training dataset and then reverses the process to recover the data, gradually learning to remove the noise and produce realistic data similar to the original input. Examples include Stable Diffusion and Midjourney.
Responsible AI
Generative AI is an exciting and rapidly evolving field with many benefits and opportunities for innovation. Yet there are also challenges and risks, including issues touching on data quality and privacy, ethics, bias and fairness, and the potential for misuse and abuse by bad actors.
WWT is committed to Responsible AI — the practice of designing, developing and deploying AI systems in a way that is safe, ethical and fair. By considering the downstream risks and benefits of AI systems from a holistic perspective, organizations can often mitigate prospective harm.
At WWT, we've been helping our clients and partners implement responsible business and technology solutions related to AI for years. Our practical approach helps organizations leverage the latest AI systems to achieve results while maturing their data strategy and high-performance architecture (HPA) capabilities.